Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

<p><strong>Abstract.</strong> We study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals into the ground at different frequency (L&amp;amp;S) band and incidence angles. Penetration depth of microwave signals into the ground depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to calculate the penetration depth at L- and C-band in soil and compared their results. We found that both of these models give different penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L- and S-band sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency.</p>

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